Lotka–Volterra Dynamics and Sustainable Regulation of Agroecosystems: Coupled Framework of Monte Carlo Simulation and Multi-Objective Optimisation

Zhiyuan Zhou, Peng Lin, Tianqi Gao, Congjie Tan, Kai Wei () and Liangzhu Yan ()
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Zhiyuan Zhou: National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
Peng Lin: National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
Tianqi Gao: National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
Congjie Tan: National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
Kai Wei: National Engineering Research Center for Oil & Gas Drilling and Completion Technology, Yangtze University, Wuhan 430100, China
Liangzhu Yan: State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2025, vol. 17, issue 10, 1-25

Abstract: Addressing the dual challenges of agricultural productivity and ecological sustainability, this study develops an integrated framework combining Lotka–Volterra dynamics, Monte Carlo simulation, and multi-objective optimisation to quantify agroecosystem responses under anthropogenic interventions. Key innovations include the incorporation of carbon sequestration dynamics and low-carbon agricultural practices into ecological–economic trade-off analysis. Our findings demonstrate the following: (1) Seasonal carbon fertilisation effects enhance producer growth by up to 30%, while energy recycling from consumer mortality offsets 22% of pesticide-induced carbon emissions. (2) The strategic introduction of dual-function species synergistically improves carbon sink capacity by 18–25% through enhanced producer efficiency and reduced chemical reliance. (3) Multi-objective optimisation reveals that integrated pest management coupled with organic amendments achieves a 51.2% net benefit improvement, while reducing agrochemical carbon footprints by 40–55%. The proposed framework bridges critical gaps in sustainable agriculture by simultaneously addressing yield stability, biodiversity conservation, and climate mitigation imperatives. This work advances the dynamic modelling of agroecosystems through probabilistic risk assessment and carbon-aware decision-making, providing actionable pathways for low-carbon agricultural intensification.

Keywords: Lotka–Volterra; dynamic evolution; Monte Carlo simulation; Pareto; low-carbon (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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